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OMNI-LEAK: Orchestrator Multi-Agent Network Induced Data Leakage

arXiv:2602.13477v1 Announce Type: new Abstract: As Large Language Model (LLM) agents become more capable, their coordinated use in the form of multi-agent systems is anticipated to emerge as a practical paradigm. Prior work has examined the safety and misuse risks associated with agents. However, much of this has focused on the single-agent case and/or setups missing basic engineering safeguards such as access control, revealing a scarcity of threat modeling in multi-agent systems. We investigate the security vulnerabilities of a popular multi-agent pattern known as the orchestrator setup, in which a central agent decomposes and delegates tasks to specialized agents. Through red-teaming a concrete setup representative of a likely future use case, we demonstrate a novel attack vector, OMNI-LEAK, that compromises several agents to leak sensitive data through a single indirect prompt injection, even in the \textit{presence of data access control}. We report the susceptibility of frontier

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Akshat Naik, Jay Culligan, Yarin Gal, Philip Torr, Rahaf Aljundi, Alasdair Paren, Adel Bibi · · 1 min read · 2 views
#cs.AI

arXiv:2602.13477v1 Announce Type: new Abstract: As Large Language Model (LLM) agents become more capable, their coordinated use in the form of multi-agent systems is anticipated to emerge as a practical paradigm. Prior work has examined the safety and misuse risks associated with agents. However, much of this has focused on the single-agent case and/or setups missing basic engineering safeguards such as access control, revealing a scarcity of threat modeling in multi-agent systems. We investigate the security vulnerabilities of a popular multi-agent pattern known as the orchestrator setup, in which a central agent decomposes and delegates tasks to specialized agents. Through red-teaming a concrete setup representative of a likely future use case, we demonstrate a novel attack vector, OMNI-LEAK, that compromises several agents to leak sensitive data through a single indirect prompt injection, even in the \textit{presence of data access control}. We report the susceptibility of frontier models to different categories of attacks, finding that both reasoning and non-reasoning models are vulnerable, even when the attacker lacks insider knowledge of the implementation details. Our work highlights the importance of safety research to generalize from single-agent to multi-agent settings, in order to reduce the serious risks of real-world privacy breaches and financial losses and overall public trust in AI agents.

Executive Summary

The article 'OMNI-LEAK: Orchestrator Multi-Agent Network Induced Data Leakage' explores the security vulnerabilities in multi-agent systems, particularly focusing on the orchestrator setup where a central agent delegates tasks to specialized agents. The study identifies a novel attack vector, OMNI-LEAK, which can compromise multiple agents to leak sensitive data through indirect prompt injection, even in the presence of data access controls. The research highlights the susceptibility of both reasoning and non-reasoning models to various categories of attacks, emphasizing the need for safety research to evolve from single-agent to multi-agent settings to mitigate real-world risks such as privacy breaches, financial losses, and erosion of public trust in AI agents.

Key Points

  • ▸ Introduction of OMNI-LEAK as a novel attack vector in multi-agent systems.
  • ▸ Demonstration of vulnerabilities in the orchestrator setup, even with data access controls.
  • ▸ Susceptibility of both reasoning and non-reasoning models to various attack categories.
  • ▸ Highlighting the importance of safety research to generalize from single-agent to multi-agent settings.

Merits

Innovative Research

The article introduces a novel attack vector, OMNI-LEAK, which is a significant contribution to the field of AI security. The research methodology, including red-teaming a concrete setup, provides a practical approach to identifying vulnerabilities.

Comprehensive Analysis

The study covers a wide range of models, including both reasoning and non-reasoning types, and demonstrates their susceptibility to different attack categories. This comprehensive analysis adds depth to the understanding of multi-agent system vulnerabilities.

Demerits

Limited Scope

The research focuses primarily on the orchestrator setup, which may not be representative of all multi-agent systems. The findings might not be generalizable to other multi-agent architectures.

Assumptions and Constraints

The study assumes certain engineering safeguards and access control mechanisms, which might not be universally applicable. The effectiveness of OMNI-LEAK in different contexts needs further exploration.

Expert Commentary

The article 'OMNI-LEAK: Orchestrator Multi-Agent Network Induced Data Leakage' presents a timely and critical examination of the security vulnerabilities in multi-agent systems. The introduction of OMNI-LEAK as a novel attack vector is particularly noteworthy, as it demonstrates the potential for significant data leaks even in the presence of access controls. The study's comprehensive analysis of both reasoning and non-reasoning models adds depth to our understanding of the vulnerabilities inherent in multi-agent architectures. However, the research is not without its limitations. The focus on the orchestrator setup, while representative of a likely future use case, may not capture the full spectrum of multi-agent systems. Additionally, the assumptions regarding engineering safeguards and access controls might not hold true in all contexts. Despite these limitations, the article makes a valuable contribution to the field of AI security. It underscores the urgent need for safety research to evolve from single-agent to multi-agent settings, highlighting the serious risks of privacy breaches, financial losses, and erosion of public trust. The practical and policy implications of this research are significant, calling for robust security measures, thorough security assessments, and the development of standardized safety protocols. As AI agents become more capable and widely deployed, the findings of this study serve as a critical reminder of the importance of proactive security measures to safeguard sensitive data and maintain public trust in AI systems.

Recommendations

  • ✓ Developers should prioritize the implementation of robust security measures in multi-agent systems, including regular security assessments and red-teaming exercises.
  • ✓ Policymakers should incorporate the findings of this research into regulations and guidelines for AI security, ensuring that multi-agent systems are adequately protected against potential vulnerabilities.

Sources

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